In March 2020, the World Health Organisation declared that COVID-19 was a new pandemic. This deadly virus spread and affected many countries in the world. During the outbreak, social media platforms such as Twitter contributed valuable and massive amounts of data to better assess health-related decision making. Therefore, we propose that users’ sentiments could be analysed with the application of effective supervised machine learning approaches to predict disease prevalence and provide early warnings. The collected tweets were prepared for preprocessing and categorised into: negative, positive, and neutral. In the second phase, different features were extracted from the posts by applying several widely used techniques, such as TF-IDF, Word2Vec, Glove, and FastText to capture features’ datasets. The novelty of this study is based on hybrid features extraction, where we combined syntactic features (TF-IDF) with semantic features (FastText and Glove) to represent posts accurately, which helps in improving the classification process. Experimental results show that FastText combined with TF-IDF performed better with SVM than the other models. SVM outperformed the other models by 88.72%, as well as for XGBoost, with an 85.29% accuracy score. This study shows that the hybrid methods proved their capability of extracting features from the tweets and increasing the performance of classification.

In this paper, we present a deep learning-based method for 3D face recognition. Unlike some previous works, our process does not rely on face representation methods as a proxy step to be accepted by Convolutional Neural Networks (CNNs). Applying 2D CNNs to irregular domains such as 3D meshes is challenging. Therefore, we employed 3D ShapeNets to recognize faces covering the full 3D shape since 3D face datasets are available and 3D data augmentation techniques to enlarge 3D datasets are widespread. The reduced size of 3D datasets is overcome by an appropriate 3D data augmentation to train our model. 3D ShapeNets are commonly used to recognize and analyse objects. To the best of the authors’ knowledge, this is the first time they are used for face recognition. This research work focuses on the preprocessing step. Whatever the nature of the face image is (either 2D or 3D representation), and whatever the acquisition conditions are, a 3D regular mesh of each input face image is first generated. Furthermore, meshes are converted to voxels to get the occupancy grid across all possible views. Finally, 3D ShapeNets are trained and recognition tests are performed. Indeed, 3D ShapeNets prove the efficiency and efficacy of 3D shape analysis task in 3D face recognition. The experimental results show that 3D face recognition using deep 3D CNNs such as 3D ShapeNets leads to significant improvement over the state-of-the-art performance on LFPW, BU3DFE, and FRAV3D datasets, with competitive recognition rates of 94.25%, 97.9% and 98.31% respectively.